A Complete Guide to Filtering in Vector Search
How filtering works in Qdrant: the filterable HNSW index, payload indexing, cardinality, and the conditions behind precise search.
Sabrina Aquino, David Myriel, Ewa Szyszka
July 02, 2026
Mastering Search
Go beyond the basics and master vector search with Qdrant. Learn how to combine filtering, hybrid retrieval, multivectors, and reranking to build high-quality search.
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 5: From Research to Product
Part 5 of the sparse embeddings series. We packaged the entire training pipeline from Parts 1-4 into an open-source CLI and web dashboard that fine-tunes SPLADE models for any product catalog in minutes.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 4: Specialization vs Generalization
Part 4 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Test cross-domain generalization, train a multi-domain model, and decide when to specialize vs generalize.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 3: Evaluation and Hard Negatives
Part 3 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Index products in Qdrant, run retrieval benchmarks, and implement ANCE-inspired hard negative mining for a 28% improvement over BM25.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 2: Training SPLADE on Modal
Part 2 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Build a training pipeline on Modal with persistent checkpoints, SpladeLoss, and hyperparameter sweeps.
Thierry Damiba
March 09, 2026
Fine-Tuning Sparse Embeddings for E-Commerce Search | Part 1: Why Sparse Embeddings Beat BM25
Part 1 of a 5-part series on fine-tuning SPLADE sparse embeddings for e-commerce search. Learn how sparse embeddings outperform BM25 and dense models for product search, how SPLADE works, and why Qdrant's native sparse vector support matters.
Thierry Damiba
March 09, 2026
MUVERA: Making Multivectors More Performant
Multi-vector representations are superior to single-vector embeddings in many benchmarks. MUVERA embeddings aim to solve the problem of slow multi-vector search by creating a single-vector representation that approximates the multi-vector representation. This single vector can be used for fast initial retrieval using traditional vector search methods, and then the multi-vector representation can be used for reranking the top results.
Kacper Łukawski
September 05, 2025
Hybrid Search Revamped - Building with Qdrant's Query API
Our new Query API allows you to build a hybrid search system that uses different search methods to improve search quality & experience. Learn more here.
Kacper Łukawski
July 25, 2024
Optimizing Semantic Search by Managing Multiple Vectors
Discover the power of vector storage optimization and learn how to efficiently manage multiple vectors per object for enhanced semantic search capabilities.
Kacper Łukawski
October 05, 2022
Mastering Batch Search for Vector Optimization
Discover how to optimize your vector search capabilities with efficient batch search. Learn optimization strategies for faster, more accurate results.
Kacper Łukawski
September 26, 2022